Energy-saving optimization strategy of multi-train metro timetable based on dual decision variables: A case study of Shanghai Metro line one

Abstract In metro systems, reducing traction energy consumption and increasing the use of regenerative braking energy (RBE) are two important methods of energy-saving optimization, which are closely related to the driving strategy and timetable of the trains. In order to minimize the net traction energy consumption (i.e., the difference between traction energy and feedback energy) of trains in a metro system, an energy-saving optimization strategy of multi-train metro timetable based on double decision variables is proposed. Considering the actual situation of the short distance between the stations of the Shanghai Metro Line One (SML1) pilot network, at the driving strategy level, two optimized driving strategies of acceleration-cruising-braking (ACRB) and acceleration-coasting-braking (ACOB) are considered respectively. At the timetable level, genetic algorithm (GA) is used to optimize the decision variables of the trains. The optimization of driving strategy and timetable balances the traction energy consumption and feedback energy to minimize the net traction energy consumption of the metro system. Finally, simulation experiments were conducted based on the pilot network of the SML1. The results show that the energy consumption of the proposed strategy can be reduced by 23.28%.